Machine learning techniques for daily solar energy prediction and interpolation using numerical weather models

Abstract

SUMMARYThis article addresses two issues in solar energy forecasting from the numerical weather prediction (NWP) models using machine learning. First, we are interested in determining the relevant information for the forecasting task. With this purpose, a study has been carried out to evaluate the influence on accuracy of the number of NWP grid nodes used as input for the forecasting model, as well as their relative importance. Several machine learning (support vector machines and gradient boosting) and feature selection algorithms (linear, ReliefF, and local information analysis) have been used in this study. The second aim is to be able to predict solar energy for locations where no previous production data are available. To address this goal, an approach consisting on modeling regions in the grid is proposed. Models (aggregate models) use as input attributes the meteorological variables relevant for the region and two new inputs to identify the location of each station: the latitude and the longitude. Those models can be used to predict energy production for existing stations and for new locations, represented by latitude and longitude.

Particle swarm optimization–deep belief network–based rare class prediction model for highly class imbalance problem

“…3,4 For us to implement the prediction models accurately, it is necessary to analyze both domain knowledge and data. 1,2 Many AI-based prediction models, which use machine learning, data mining, databases, and statistical methods, are being proposed.…”

“…Many AI‐based prediction models, which use machine learning, data mining, databases, and statistical methods, are being proposed. Such prediction models based on state‐of‐the‐art techniques are being applied in many fields, and there is a progressive increase in their industrial value …”

“…3,4 For us to implement the prediction models accurately, it is necessary to analyze both domain knowledge and data. Since majority class prediction is easy, its accuracy seems to be also high.…”

“…Such prediction models based on state-of-the-art techniques are being applied in many fields, and there is a progressive increase in their industrial value. 3,4 For us to implement the prediction models accurately, it is necessary to analyze both domain knowledge and data. In addition, there is an increase in demand for obtaining useful knowledge from the collected data, and therefore, active research is being conducted on prediction models that are suitable for specific domains.…”

Desenvolvimento de classificador de código aberto para identificação de polímeros destinados à manufatura aditiva

“…Essa metodologia tem como objetivo dimensionar os recursos para casos em que os modelos são sensíveis às escalas dos recursos de entrada. Como exemplos, pode-se citar os algoritmos que operam com intervalos de valores numéricos restritos, como um sistema de localização baseado em coordenadas de latitude e longitude (MARTIN et al, 2015). No caso de dados de espectroscopia, a técnica de normalização por escala também é indispensável, tendo em vista a necessidade de padronizar os atributos de entrada, como os valores máximos e mínimos de absorbância em um intervalo de comprimento de onda.…”